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I have some data with two categorises plus/minus (p53) and a particular time (Time) and the outcome is a continuous vairable (Result). I set up a maximum model. ancova <- lm(Result~Time*p53) > summary(ancova) .. Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.05919 0.55646 0.106 0.916 Time -0.02134 0.01785 -1.195 0.241 p53plus

Hi List, I don't understand why 'lrm' doesn't recognize the '~.' formula. I'm pretty sure it was working before. Please see below: I'm using R2.3.0, WinXP, Design 2.0-12 thanks, ...Tao > dat <- data.frame(y=factor(rep(1:2,each=50)), x1=rnorm(100), x2=rnorm(100), x3=rnorm(100)) > lrm(y~., data=dat, x=T, y=T) Error in terms.formula(formula, specials =

Dear all, I would like to know if there is a function for sorting a data.frame based on one column. sort() does it for a single vector but not for the whole data. order() does it if there are relations between the columns but in my case it is simply a data.frame. Further, I would like to plot other data, pop.size<- c(800, 800, 1500, 1000, 158, 300, 740, 250, 2000, 1500, 250, 700)

Hello people, I am in the process of migrating from Stata to R and I would like to check if my results are similar under the two softwares: Here is my GLM command under R nurse.model<-glm(pQSfteHT~dQSvacrateHTQuali3_2 + dQSvacrateHTQuali3_3 + dQSvacrateHTQuali3_4 + dQSvacrateHTQuali3_5 + cluster_32 + cluster_33 + cluster_34 ,family=binomial(link = "logit")) and below the stata

Hi, In my analysis of impacts of insecticide-treated bednets on malaria, I look at the relationship between malaria incidence and mosquito behaviors. The condensed data set is copied here. Ordinary regression (lm) shows that Incidence was negatively related to Mortality. This makes sense because the latter reflected the strength of killing mosquitoes by insecticide-treated nets. Since the

Hello, I am trying to mount a lustre filesystem thru an ib2ip gateway. The MDS''s have infiniband connections. The client nodes are tcp/ip connections. I am able to route between the client nodes and the MDS''s. I have the following in /etc/fstab: abe-mds1 at o2ib0,abe-mds2 at o2ib0:/home/client /abehome lustre _netdev,flock 0 0 I get the following when trying

Hi I compouted a multiple linear regression with repeated measures on one explanatory variable: BOLD peak (blood oxygenation) as dependent variable, and as independent variables I have: -age.group (binaray:young(0)/old(1)) -and task-difficulty measured by means of the reaction-time 'rt'. For 'rt' I have repeated measurements, since each subject did 12 different tasks. -> so

Hello everybody, I am sorry if my questions are too simple or not easily understandable. I’m not a native English speaker and this is my first analysis using this function. I have a problem with a cross correlation function and I would like to understand how I have to perform it in R. I have yearly data of an independent variable (x) from 1982 to 2010, and I also have yearly data of a variable

Hi Berend, I see you are one of the contributors to the rbecnhmark package. I am sorry that I am bothering you again. I have tried to run your code (slightly tweaked) involving the benchmark function, and I am getting the following error message. What am I doing wrong? Error in benchmark(d1 <- s1(df), d2 <- s2(df), d3 <- s3(df), d4 <- s4(df), : could not find function

Hello R users, I am trying to calculate the stochastic lambda for a published matrix population model using the popbio package. Unfortunately, I have been unable to match the published results. Can anyone tell me whether this is due to slightly different methods being used, or have I gone wrong somewhere in my code? Could the answer be as simple as comparing deterministic lambdas to

Hello, Apologies if this is the wrong list, I am a first-time poster here. I have an experiment in which an output is measured in response to 42 different categories. I am only interested which of the categories is significantly different from a reference category. Here is the summary of the results: summary(simple.fit) Call: lm(formula = as.numeric(as.vector(TNFa)) ~ Mutant.ID, data =

On Tue, Jul 30, 2019 at 11:54:53AM -0400, Michael S. Tsirkin wrote: > On Tue, Jul 30, 2019 at 05:43:29PM +0200, Stefano Garzarella wrote: > > This series tries to increase the throughput of virtio-vsock with slight > > changes. > > While I was testing the v2 of this series I discovered an huge use of memory, > > so I added patch 1 to mitigate this issue. I put it in this

Hi folks, Warning: I don't know if the result I am getting makes sense, so this may be a statistics question. The fitted values from my binomial lmer mixed model seem to consistently overestimate the cell means, and I don't know why. I assume I am doing something stupid. Below I include code, and a binary image of the data is available at this link:

Hi. I'm attempting to fit a logistic/binomial model so I can determine the influence of landscape on the probability that a box gets used by a bird. I've looked at a few sources (MASS text, Dalgaard, Fox and google) and the examples are almost always based on tabular predictor variables. My data, however are not. I'm not sure if that is the source of the problems or not because the

Dear list, I am currently working with a rather large data set on body temperature regulation in wintering birds. My original model contains quite a few dependent variables, but I do not (of course) wish to keep them all in my final model. I've fitted the following model to the data: >

I'm still learning how to program with R and I was hoping someone could take the time to show me how I can rewrite this code? Many thanks Tom data.intersects<-data.frame( x=c(0.230,0.411,0.477,0.241,0.552,0.230), y=c(0.119,0.515,0.261,0.431,0.304,0.389), angle=vector(length=6), length=vector(length=6),

hi everyone! i want to check the autocorrelation function for a univariate time series (streamflow) in a data frame as below: < DF <- read.table("D:/file path....") < DF year jan feb mar apr ...... dec 1966 0.504 0.406 0.740 0.241 0.429 1967 0.683 0.529 0.780 0.443 0.503 . . . . what i first tried is: acf (DF, plot = TRUE)

Dear R-users; I'm working with a a dataset that was previously used to fit a nonlinear model of the form: Y ~ a * (1 + b * log(1 - c * X^d)) The parameters published elsewhere are: a = 1.758863, b = .217217, c = .99031, and d = .054589 However, there is no way I can replicate this result. I've tried several options (including SAS) w/o success. The data is: X <-

Hello R-help subscribers, I am analyzing a data set using a mixed logit model, and I have recently discovered some curious behavior. I am hoping you all can help. I first ran the following model in December 2012. lmer(Response.binary ~ ItemType.c * Block + (1 | Subject) + (1 | Word), data=lexdec, family="binomial") I then took a break from the data for the holidays. I returned to

Hi r-users,   I would like to extract the data that match.  Attached is my data: I'm interested in matchind the value in column 'intg' with value in column 'rand_no' > cbind(z=z,intg=dd,rand_no = rr)             z  intg rand_no    [1,]  0.00 0.000   0.001    [2,]  0.01 0.000   0.002    [3,]  0.02 0.000   0.002    [4,]  0.03 0.000   0.003    [5,]  0.04 0.000   0.003    [6,]